TARGETLESS REGISTRATION METHODS BETWEEN UAV LIDAR AND WEARABLE MMS POINT CLOUDS

Fixed-wing Unmanned Aerial Vehicles (UAV) and wearable or portable Mobile Mapping Systems (MMS) are two widely used platforms for point cloud acquisition with Light Detection And Ranging (LiDAR) sensors. The two platforms acquire from distant viewpoints and produce complementary point clouds, one describing predominantly horizontal surfaces and the other primarily vertical. Thus, the registration of the two data is not straightforward. This paper presents a test of targetless registration between a UAV LiDAR point cloud and terrestrial MMS surveys. The case study is a vegetated hilly landscape characterized by the presence of a structure of interest; the UAV acquisition allows the entire area to be acquired from above, while the terrestrial MMS acquisitions will enable the construction of interest to be detailed. The paper describes the survey phase with both techniques. It focuses on processing and registration strategies to fuse the two data together. Our approach is based on the ICP (Iterative Closest Point) method by exploiting the data processing algorithms available in the Heron Desktop post-processing software for handling data acquired with the Heron Backpack MMS instrument. Two co-registration methods are compared. Both ways use the UAV point cloud as a reference and derive the registration of the terrestrial MMS data by finding ICP matches between the ground acquisition and the reference cloud exploiting only a few areas of overlap. The two methods are detailed in the paper, and both allow us to complete the co-registration task

Recent evolution of the Punta San Matteo serac (Ortles-Cevedale Group, Italian Alps)

This paper summarizes the main results of surveys carried out on the Punta San Matteo serac (Ortles-Cevedale Group, Italy). The monitoring campaigns mainly consisted in surveying the serac with a Total Station (over the period from July 2005 to November 2005) and with a laser scanner. The displacements of the unstable ice mass (about 12 m) and its geometry and volume (about 560,000 m') have been calculated. In addition several photographs collected during the field campaigns made it possible to describe the evolution of this unstable ice mass and recorded its partial collapse and gradual breaking into tiny parts. The air temperature trend was also evaluated; the serac displacements resulted not strongly correlated with temperature evolution and the main falling events occurred in the autumn and not in summer when air temperature reached the highest peaks

INDOOR MOBILE MAPPING SYSTEMS AND (BIM) DIGITAL MODELS FOR CONSTRUCTION PROGRESS MONITORING

Technological developments of the last decades are making possible to speed up different processes involved in construction projects. It is noticeable what building information modeling (BIM) can offer during the entire lifecycle of a project by integrating graphical and non graphical data, in addition to this, mapping the site with a 3D laser scan has been proved to provide a feasible workflow to compare as built models with as designed BIM, in this way, an automatic construction progress monitoring can also be performed. Terrestrial laser scanners (TLS) are commonly used to map a construction site due the level of accuracy provided, but indoor mobile mapping systems (iMMS) could offer a more efficient approach by speeding up the acquisition time and capturing all the details of the site just by walking through it, provided that the point cloud is accurate enough for the purpose of interest. In this paper, an iMMS is used to track the progress of a construction site, the point clouds were uploaded onto a platform of autonomous construction progress monitoring to verify if the system can meet the requirements of available applications. The results showed that the iMMS used is capable to produce point clouds with a quality such that the construction progress monitoring can be performed

A vegetations map of south America.

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Tropical Forest Mapping at Regional Scale Using the GRFM SAR Mosaics over Amazon in South America

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Tropical forest mapping at regional scale using the GRFM SAR mosaics over the Amazon in South America

The work described in this thesis concerns the estimation of tropical forest vegetation cover in the Amazon region using as data source a continental scale high resolution (100 m) radar mosaic as data source. The radar mosaic was compiled by the Jet Propulsion Laboratory (NASA JPL) using approximately 2500 JERS-1 L-band scenes acquired in the context of the Global Rain Forest Mapping project by the National Agency for Space Development of Japan (NASDA).A novel classification scheme was developed for this purpose.The underpinning method is based on a wavelet signal decomposition/reconstruction technique. In the wavelet reconstruction algorithm, an adaptive wavelet coefficient threshold is introduced to distinguish wavelet maxima related to the transition between classes from maxima related to textural within-class variation.Two image-labeling techniquesare tested and compared: i) a region-growing algorithm and ii) a per-pixel two-stage hybrid classifier.The large data volume problem was tackled by developing a special purpose processing chain that works on partially overlapping tiles extracted from the mosaicQuantitative validation and error analysis of the classifiers' performance and their generalization capability to regional scale are carried out using, as reference, maps derived from Landsat Thematic Mapper. A first result of the validation process is that the wavelet classifier provides a classification accuracy of 87% in forest/non-forest mapping. The analysis by site reveals that class degraded-forest is the major source of classification errors. The discrepancy between TM maps and SAR maps increases with increasing landscape spatial fragmentation.A test on relative performances between the wavelet-based region growing segmentation technique and a conventional clustering technique (ISODATA) shows that the wavelet-based technique provides better accuracy and is capable of generalizing over the entire data set.The issue of detecting the degraded-forest class - generally ignored by Amazonian deforestation mapping programs - is tackled using data acquired by both optical and SAR instruments . For optical data, a three-stage classification procedure is developed for detecting degraded forest classes in Landsat TM images. For SAR data, a multi-temporal speckle filtering technique is used to improve the signal to noise ratio.Forestdegradation, characterized by small isolated and elongated bare soil regions regularly distributed in forest areas, is visually detectable in the filtered imagery.Starting from the consideration that the discrepancy between TM maps and SAR maps increases with the landscape spatial fragmentation we test an inductive learning methodology, capable of correcting SAR regional-scale maps using local classification estimates at a higher resolution , is tested.Finally some ideas and projects are put forward which are meant to be working hypotheses for future actions and practical approaches to reduce the pressure over the tropical forest ecosystem